Image segmentation is one of the most challenging problems faced by many medical imaging applications despite four decades of advance in the field. Segmentation methods should produce accurate, repeatable, and efficient segmentations of medical images which will lead to more effective visualization, manipulation and analysis of anatomic structures (objects). Segmentation consists of two tightly coupled tasks; recognition and delineation. Recognition is the process of identifying roughly the whereabouts of a particular object of interest and distinguishing it from other objects present in the image. Delineation is the process of specifying the precise spatial extent of the object.
Existing methods to delineate the boundary of objects suffer from several limitations. Purely model-based segmentation methods require that a template shape be placed quite close to the object to segment. Other methods have insufficient accuracy in detecting the border of the object or require many manually placed landmarks. One well known segmentation method is the Active Shape Models (ASM) method.
ASM-based approaches have been used in several segmentation tasks in medical imaging. However, in practice, ASM methods still face four main difficulties: (1) Since the segmentation results are parametric descriptions of the identified shape, they often poorly match the perceptually identifiable boundary in the image. Contour segments between landmarks still need to be determined. These inaccuracies pose problems for the subsequent analysis of medical images. (2) In order to model the shape reasonably well, the ASM needs many landmark points and training samples to represent the shape and its variation. This is costly and time consuming in the training phase. In some cases, sufficient training samples are not available. (3) Due to the local search strategy, ASM segmentation results are sensitive to the search region around each landmark point. If the region is too small, the ASM may not reach the true boundary. If the region is too large, the landmark is misplaced on a similar looking boundary, and part of the contour may be attracted towards a neighboring structure. (4) Also due to the local nature of the search strategy, the ASM approaches are sensitive to inaccurate initialization and may not converge to a correct segmentation. (5) More importantly, by nature, model based matching has to rely on matching (blurred) statistical information in the model to the given image. Therefore the specific information present in the given image cannot be made use of in a specific manner as in purely image based strategies. This affects how best landmark positions can be determined on a given image, and thus has consequences also on items (1)-(4) above. It is desired to realize an approach that can improve on the ASM methods by improving the accuracy of a segmentation process while reducing the total amount of human interaction. The present invention addresses these concerns and others.